Automatic cross-sectioning based on topological volume skeletonization

Cross-sectioning is a popular method for visualizing the complicated inner structures of three-dimensional volume datasets. However, the process is usually manual, meaning that a user must manually specify the cross-section's location using a repeated trial-and-error process. To find the best cross-sections, this method requires that a user is knowledgeable and experienced. This paper proposes a method for automatically generating characteristic cross-sections from a given volume dataset. The application of a volume skeleton tree (VST), which is a graph that delineates the topological structure of a three-dimensional volume, facilitates the automated generation of cross-sections giving good representations of the topological characteristics of a dataset. The feasibility of the proposed method is demonstrated using several examples.